Conference Publications

1. DATE

   PFASware: Quantifying the Environmental Impact of Per- and Polyfluoroalkyl Substances (PFAS) in Computing Systems

   Mariam Elgamal, Abdulrahman Mahmoud, Gu-Yeon Wei, David Brooks, and Gage Hills

   In 2025 Design, Automation and Test in Europe Conference (to appear), 2025
2. DATE
   Best Paper Award Candidate

   Quantifying Trade-Offs in Power, Performance, Area, and Carbon Footprint of Future Computing Systems

   Danielle Grey-Stewart, Mariam Elgamal, David Kong, Georgios Kyriazidis, Jalil Morris, and Gage Hills

   In 2025 Design, Automation and Test in Europe Conference (to appear), 2025
3. HPCA

   CORDOBA: Carbon-Efficient Design Optimization Framework for Computing Systems

   Mariam Elgamal, Doug Carmean, Elnaz Ansari, Okay Zed, Ramesh Peri, Srilatha Manne, Udit Gupta, Gu-Yeon Wei, David Brooks, Gage Hills, and Carole-Jean Wu

   In 2025 IEEE International Symposium on High-Performance Computer Architecture (to appear), 2025
4. ISCA
   IEEE MICRO Top Picks

   ACT: Designing Sustainable Computer Systems with an Architectural Carbon Modeling Tool

   Udit Gupta, Mariam Elgamal, Gage Hills, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, and Carole-Jean Wu

   In Proceedings of the 49th Annual International Symposium on Computer Architecture, 2022

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   Given the performance and efficiency optimizations realized by the computer systems and architecture community over the last decades, the dominating source of computing’s carbon footprint is shifting from operational emissions to embodied emissions. These embodied emissions owe to hardware manufacturing and infrastructure-related activities. Despite the rising embodied emissions, there is a distinct lack of architectural modeling tools to quantify and optimize the end-to-end carbon footprint of computing. This work proposes ACT, an architectural carbon footprint modeling framework, to enable carbon characterization and sustainability-driven early design space exploration. Using ACT we demonstrate optimizing hardware for carbon yields distinct solutions compared to optimizing for performance and efficiency. We construct use cases, based on the three tenets of sustainable design—Reduce, Reuse, Recycle—to highlight future methods that enable strong performance and efficiency scaling in an environmentally sustainable manner.

Peer-Reviewed Workshop Publications

1. HotEthics

   The Environmental Impact of Forever Chemicals in Computing Systems

   Mariam Elgamal, Abdulrahman Mahmoud, Gu-Yeon Wei, David Brooks, and Gage Hills

   2024

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2. HotCarbon

   Carbon-Efficient Design Optimization for Computing Systems

   Mariam Elgamal, Doug Carmean, Elnaz Ansari, Okay Zed, Ramesh Peri, Srilatha Manne, Udit Gupta, Gu-Yeon Wei, David Brooks, Gage Hills, and Carole-Jean Wu

   2023

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   The world’s push toward an environmentally sustainable society is highly dependent on the semiconductor industry, due to carbon footprints of global-scale sources such as computing systems for virtual and extended reality applications (VR and XR). Despite previous carbon modeling efforts for such computing systems, there lacks a wide range of design tools to optimize total life cycle carbon footprint (during manufacturing and also during day-to-day operation), while meeting application-level constraints (power, performance, area). To address this need, we have developed a carbon-aware design framework that optimizes carbon efficiency of computing systems—quantified by metrics such as total Carbon Delay Product (tCDP: the product of total carbon and total application execution time)—while also identifying key design parameters for improving carbon efficiency. As a case study, we demonstrate the effectiveness of our framework to improve tCDP of hardware accelerators for artificial intelligence (AI) and XR applications. We show: (1) optimizing for carbon efficiency (tCDP) instead of energy efficiency (Energy-Delay Product or EDP) improves carbon efficiency by up to 6.9x—i.e., optimizing for EDP is insufficient; (2) for multi-core CPUs inside production VR headsets, optimizing number of cores (from 8 to 4) improves tCDP by 1.25x (over their entire lifetime); (3) leveraging an advanced three-dimensional integration (3D) technique (3D stacking of separately-fabricated logic and memory chips) can improve tCDP by 6.9x vs. conventional systems (no 3D stacking).

Journal Publications

1. J. Phys. Chem. C

   Ternary Ti–Mo–Fe Nanotubes as Efficient Photoanodes for Solar-Assisted Water Splitting

   Abdussalam M. Elbanna, Kholoud E. Salem, Abdelrahman M. Mokhtar, Mohamed Ramadan, Mariam Elgamal, Hussein A. Motaweh, Hassan M. Tourk, Mohamed A.-H. Gepreel, and Nageh K. Allam

   The Journal of Physical Chemistry C, 2021

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2. J. Comps. Mater.

   Tailoring Composite Materials for Nonlinear Viscoelastic Properties Using Artificial Neural Networks

   Xianbo Xu, Mariam Elgamal, Mrityunjay Doddamani, and Nikhil Gupta

   Journal of Composite Materials, 2021

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   Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus (E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems.